{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 产生分隔的高斯分布的聚类簇 样本分布见 6-1\n",
    "# centers 核心点的数组\n",
    "# num 样本数\n",
    "# std 每簇的标准差\n",
    "def create_data(centers,num=1000,std=0.7):\n",
    "    x, labels_true = make_blobs(n_samples=num, centers=centers, cluster_std=std)\n",
    "    return x, labels_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_DBSCAN(*data):\n",
    "    x,labels_true = data\n",
    "    dbscan = cluster.DBSCAN()\n",
    "    predicted_labels = dbscan.fit_predict(x)\n",
    "    print('ARI:{0}'.format(adjusted_rand_score(labels_true,predicted_labels))) #ARI 可以理解为聚类效果 越大越好\n",
    "    print('Core sample num: {0}'.format(len(dbscan.core_sample_indices_))) # 根据密度划分了多少个簇\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI:0.33145663777392786\n",
      "Core sample num: 991\n"
     ]
    }
   ],
   "source": [
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "test_DBSCAN(x, labels_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 参数epsilon的影响\n",
    "def test_DBSCAN_epsilon(*data):\n",
    "    x,labels_true = data\n",
    "    epsilons = np.logspace(-1,1.5)\n",
    "    nums = range(1,15)\n",
    "    ARIs =[]\n",
    "    Core_nums = []\n",
    "    for epsilon in epsilons:\n",
    "        dbscan = cluster.DBSCAN(eps=epsilon)\n",
    "        predicted_labels = dbscan.fit_predict(x)\n",
    "        ARIs.append(adjusted_rand_score(labels_true,predicted_labels))\n",
    "        Core_nums.append(len(dbscan.core_sample_indices_))\n",
    "        \n",
    "    # 绘图\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(10)\n",
    "    fig.set_figwidth(30)\n",
    "    ax = fig.add_subplot(121)\n",
    "    ax.plot(epsilons, ARIs, marker='+')\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_xlabel(r'$\\epsilon$')\n",
    "    ax.set_ylabel('ARI')\n",
    "    ax.set_ylim(0,0.4)\n",
    "    \n",
    "    ax = fig.add_subplot(122)\n",
    "    ax.plot(epsilons, Core_nums, marker='o')\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_xlabel(r'$\\epsilon$')\n",
    "    ax.set_ylabel('Core_nums')\n",
    "    fig.suptitle('DBSCAN')\n",
    "    plt.show()\n",
    "\n",
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "test_DBSCAN_epsilon(x, labels_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# minpts的影响\n",
    "def test_DBSCAN_min_samples(*data):\n",
    "    x,labels_true = data\n",
    "    min_samples = range(1,100)\n",
    "\n",
    "    \n",
    "    ARIs =[]\n",
    "    Core_nums = []\n",
    "    for num in min_samples:\n",
    "        dbscan = cluster.DBSCAN(min_samples=num)\n",
    "        predicted_labels = dbscan.fit_predict(x)\n",
    "        ARIs.append(adjusted_rand_score(labels_true,predicted_labels))\n",
    "        Core_nums.append(len(dbscan.core_sample_indices_))\n",
    "        \n",
    "    # 绘图\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(10)\n",
    "    fig.set_figwidth(30)\n",
    "    ax = fig.add_subplot(121)\n",
    "    ax.plot(min_samples, ARIs, marker='+')\n",
    "    ax.set_xlabel(r'min_samples')\n",
    "    ax.set_ylabel('ARI')\n",
    "    ax.set_ylim(0.2,0.4)\n",
    "    \n",
    "    ax = fig.add_subplot(122)\n",
    "    ax.plot(min_samples, Core_nums, marker='o')\n",
    "    ax.set_xlabel(r'min_samples')\n",
    "    ax.set_ylabel('Core_nums')\n",
    "    fig.suptitle('DBSCAN')\n",
    "    plt.show()\n",
    "\n",
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "test_DBSCAN_min_samples(x, labels_true)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
